Jobs at Paperless Parts, Inc.

Build and support customer integrations connecting Paperless Parts with CRM, ERP, and MES platforms. Lead technical discovery, translate business requirements into scalable solutions, deploy integrations, manage concurrent delivery projects, and develop reusable integration components and tooling. The role requires strong Python, API, web services, SQL, and customer-facing technical execution skills, with preferred experience in manufacturing systems, CI/CD, Docker, Jenkins, and AWS.

Build and support customer integrations connecting Paperless Parts with CRM, ERP, and MES platforms. Lead technical discovery, translate business requirements into scalable solutions, deploy integrations, and manage concurrent technical delivery projects. Develop reusable integration components, templates, and tooling to improve delivery efficiency. The role requires Python, API, web services, SQL, and integration architecture expertise, with preferred experience in manufacturing systems, CI/CD, Docker, Jenkins, and AWS.

Build and maintain customer pricing models and integrations using P3L, a Python-based language. Translate manufacturing pricing requirements into software configurations, conduct technical discovery, document solutions, collaborate with implementation teams, support post-launch optimization, and advise customers on quoting workflows. The role is hybrid in Encinitas, California, includes up to 25% travel to customer manufacturing facilities, and requires U.S. citizenship or permanent residency due to export control requirements.

Build and maintain customer pricing models and integrations using the proprietary P3L Python-based language. Translate manufacturing pricing requirements into software configurations, conduct technical discovery, document solutions, collaborate with project managers and engineers, support customer onboarding, and optimize live implementations. The role includes customer consulting, on-site manufacturing facility visits, and post-launch technical support.

Manage a portfolio of enterprise customers, driving retention, expansion, software adoption, ROI realization, and long-term success. Build executive and operational relationships, lead multi-site rollouts, conduct business reviews, resolve escalations, and collaborate with Sales, Product, Implementation, and Support. The role requires manufacturing domain knowledge, technical aptitude, strategic account planning, and up to 50% travel.

Leads customer experience operations across Professional Services, Customer Success, Support, and Managed Services. Owns capacity planning, utilization, forecasting, backlog management, analytics, executive reporting, systems integration, workflow automation, and operational accountability. Partners with Sales, Finance, Product, and Business Operations to align delivery capacity with growth, improve data quality, increase tool adoption, and drive consistent customer outcomes.

Lead enterprise SaaS implementations for manufacturing customers, managing timelines, scope, budgets, risks, resources, and executive communications. Translate customer requirements into platform configurations, facilitate training and workshops, manage change, resolve issues, and drive software adoption. The role also identifies deployment lifecycle gaps and develops scalable workflow improvements while collaborating with customer stakeholders and internal engineering teams.

Leads a 25+ person engineering organization through two Directors, overseeing delivery quality, technical architecture, platform scalability, AI-enabled engineering practices, and new product development. Partners with Product and the Chief Scientist to align roadmaps, launch new manufacturing intelligence products, improve platform efficiency and reliability, develop engineering leaders, and represent engineering strategy and risks to executives.

Own and improve production cloud infrastructure on AWS using OpenTofu and Terragrunt. Manage Jenkins CI/CD pipelines, develop Python or Bash automation, maintain Dockerized services, extend Datadog monitoring, and participate in on-call operations, code reviews, sprint ceremonies, and engineer onboarding. The role offers end-to-end ownership of infrastructure components and growth toward senior platform engineering.

Lead enterprise SaaS implementations for manufacturing customers, managing timelines, resources, scope, budgets, risks, and executive communications. Translate customer requirements into platform configurations, guide change management, deliver training and workshops, resolve issues, and improve deployment workflows. The role requires onsite work in Encinitas, California three days per week, customer travel one to three times monthly, and eligibility under US export control laws through US citizenship or green card status.

Lead enterprise SaaS implementations for manufacturing customers, managing timelines, budgets, scope, risks, resources, executive stakeholders, training, workshops, and change management. Translate business requirements into platform configurations, resolve issues, support adoption, and improve deployment workflows. The role requires Boston-based hybrid work, customer-site travel, and US citizenship or permanent residency due to export-control requirements.

Own the Canadian territory’s go-to-market strategy and full-cycle SaaS sales process, from outbound prospecting and discovery through negotiation and closing. Build pipeline, engage manufacturing executives, conduct ROI modeling, maintain CRM hygiene and forecasts, and collaborate with Sales Engineering, Marketing, Product, and Customer Success. Represent the company at Canadian trade shows, customer visits, and industry events while supporting long-term customer retention.

Own a book of strategic B2B SaaS customers to drive product adoption, retention, renewals, and expansion. Act as trusted advisor to executives, manage escalations, forecast renewal/expansion outcomes, identify growth opportunities, and collaborate across Sales, Product, and Support to improve customer ROI and outcomes.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.